BIM4EarlyLCA: An interactive visualization approach for early design support based on uncertain LCA results using open BIM

Forth, Kasimir; Hollberg, Alexander; Borrmann, André

doi:10.1016/j.dibe.2023.100263

Cited by 9 publications

(5 citation statements)

References 27 publications

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“…Enriching BIM models, particularly ML-based image recognition, has extended BIM's capabilities, enhancing its semantic richness and facilitating a deeper understanding of construction elements [26,71]. This progression points towards the potential of BIM across the construction lifecycle, driving industry-wide adoption and innovation [3,6].…”

Section: Discussionmentioning

confidence: 99%

“…The practical implications of image recognition innovation into BIM systems extend significantly beyond theoretical advancements for industry professionals to be considered as future work. Moreover, the ability to automatically update and enrich BIM models with real-world images aids in maintaining the relevance and accuracy of the model throughout the building's lifecycle, offering benefits in facility management and renovation planning [3]. The adoption of such technologies by industry professionals can lead to improved collaboration, where decision-making is supported by data-rich, accurate BIM models.…”

Section: Discussionmentioning

confidence: 99%

“…In the rapidly evolving digital era, the construction industry is experiencing a significant shift towards model-centric and data-driven workflows [1,2]. The digitalization of the construction industry offers remarkable benefits, including informed decision-making, enhanced safety and accuracy, improved integration and communication, cost reduction, increased transparency, and better prediction and control throughout the building lifecycle [3,4]. In this transformation, Building Information Modeling (BIM) emerges as a key component in supporting essential functions for construction project teams and in building advanced digital twins and data analytics pipelines [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Semantic Enrichment of BIM: The Role of Machine Learning-Based Image Recognition

Mirarchi,

Gholamzadehmir,

Daniotti

et al. 2024

Buildings

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Building Information Modelling (BIM) revolutionizes the construction industry by digitally simulating real-world entities through a defined and shared semantic structure. However, graphical information included in BIM models often contains more detailed data compared to the corresponding semantic or computable data. This inconsistency creates an asymmetry, where valuable details present in the graphical renderings are absent from the semantic description of the model. Such an issue limits the accuracy and comprehensiveness of BIM models, constraining their full utilization for efficient decision-making and collaboration in the construction process. To tackle this challenge, this paper presents a novel approach that utilizes Machine Learning (ML) to mediate the disparity between graphical and semantic information. The proposed methodology operates by automatically extracting relevant details from graphical information and transforming them into semantically meaningful and computable data. A comprehensive empirical evaluation shows that the presented approach effectively bridges the gap between graphical and computable information with an accuracy of over 80% on average, unlocking the potential for a more accurate representation of information within BIM models and enhancing decision-making and collaboration/utility in construction processes.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Semantic Enrichment of BIM: The Role of Machine Learning-Based Image Recognition

Mirarchi,

Gholamzadehmir,

Daniotti

et al. 2024

Buildings

View full text Add to dashboard Cite

show abstract

“…Additionally, BIM viewers enhance the visualization of LCI libraries by viewing and exploring BIM models in 3D. Open BIM data formats and model-based LCA visualizations (including color-coded IFC model viewers) are widely accepted and demanded, but the acceptance of uncertainty visualizations is low, which requires further research [86]. It may be inferred by watching the computations and examining the BOQ that fluctuations in the quantity of material and streamlined calculating procedures are the causes of outcome variances.…”

Section: Bim-integrated Lca Applicationmentioning

confidence: 99%

Recent Technological Advancements in BIM and LCA Integration for Sustainable Construction: A Review

Chen,

Zhou

et al. 2024

Sustainability

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In the high-energy, high-carbon landscape of the construction industry, a detailed and precise life cycle assessment (LCA) is essential. This review examines the role of building information modeling (BIM) software in streamlining the LCA process to enhance efficiency and accuracy. Despite its potential, challenges such as software interoperability and compatibility persist, with no unified standard for choosing BIM-integrated LCA software. Besides, the review explores the capabilities and limitations of various BIM software, LCA tools, and energy consumption tools, and presents characteristics of BIM-LCA integration cases. It critically discusses BIM-LCA integration methods and data exchange techniques, including bill of quantities import, Industry Foundation Classes (IFC) import, BIM viewer usage, direct LCA calculations with BIM plugins, and LCA plugin calculations. Finally, concluding with future perspectives, the study aims to guide the development of advanced LCA tools for better integration with BIM software, addressing a vital need in sustainable construction practices.

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“…The calculation model created through the use of a BIM platform (Building Information Modeling) [41][42][43][44], based on national legislation, has consequently obtained results based on the energy budget only. These results do not take into account, for example, an analysis of the life cycle (Life Cycle Assessment, LCA) of the materials, an aspect developed by numerous authors for these types of materials [45][46][47], even with the use of BIM [48][49][50][51]. The choice of this evaluation is made as a consequence of the fact that on the national scene, to date, the evaluations on energy transfers do not consider energy-environment efficiency indicators that take into account the energy cost for the production of the materials used.…”

Section: Introductionmentioning

confidence: 99%

Energetic Performance of Natural Building Materials: Numerical Simulation and Experimental Evaluation

Mastino,

Concu,

Frattolillo

2024

Energies

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The current goal of the European Commission, which aims to reduce CO2 by 90% compared to values estimated in 1980, and the ever-increasing sensitivity to environmental sustainability, fully involve the construction sector, which, according to the OECD (Organization for Economic Co-Operation and Development) is responsible for over one-third of the world’s energy requirement. In this frame, numerous researchers and companies are focusing on ecologically sustainable building materials, to be used in new and existing buildings, that are able to simultaneously fulfill the constructive function and improve the energy behavior of the building envelope. The goal of the present paper is the analysis of the energy performance of some innovative locally produced natural building materials (timber, sheep wool, rammed earth, lime-based plaster, natural fibers) used in multilayer vertical closures, compared to that of more common building materials (bricks, concrete, synthetic insulation). First, the physical-mechanical characterization of the local natural materials was carried out, then the model of a building was implemented, whose energetic performance was simulated by varying the type of stratigraphy of the walls, including the use of both innovative and common materials. The building chosen for the simulation consists of one of the BESTEST ANSI/ASHRAE reported in the 140-2017 standard using the climatic data of the Mediterranean area. The results of the simulation have been presented and discussed.

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BIM4EarlyLCA: An interactive visualization approach for early design support based on uncertain LCA results using open BIM

Cited by 9 publications

References 27 publications

Semantic Enrichment of BIM: The Role of Machine Learning-Based Image Recognition

Semantic Enrichment of BIM: The Role of Machine Learning-Based Image Recognition

Recent Technological Advancements in BIM and LCA Integration for Sustainable Construction: A Review

Energetic Performance of Natural Building Materials: Numerical Simulation and Experimental Evaluation

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